What Happens Before a Spectrum-X AI Fabric Goes Live?



The AI network doesn't just begin when the first GPU is plugged into a switch. A day zero in NVIDIA Spectrum-X involves topology definition, IP assignment, BGP setup, QoS settings, etc. At scale, the human effort that goes into configuring all of these components can turn into a substantial engineering effort.

Intent-based automation can alleviate these difficulties.

Starting with basic requirements such as Scale Unit and number of GPUs, IP ranges to utilize, Aviz ONES Fabric Designer can generate both the design of the fabric and configuration for the desired environment. This takes care of things on both ends of the system, from the IPCLOS underlay (leaf and spine setup, BGP configuration, ASN assignment, IP address, interfaces and MTU setup) all the way up to server-side setup (RoCEv2 parameters with PFC, and ECN, BlueField-3 settings, etc.). But setting up is only one hurdle. Validation is the other, so using NVIDIA AIR enables validation of the design, before rolling it out into actual hardware.

Design-->Validation-->Deploy workflow, the aim is to minimize and remove a dependence upon manually configuring items.

This is where the idea of the AI factory comes in. An AI factory brings compute, networking, storage, and orchestration together as one connected system, rather than separate pieces stitched together after the fact. Aviz ONES implements this end to end, spanning DGX, HGX, and NVL systems along with the broader partner ecosystem of compute, storage, orchestration, NICs, and networking. It gives unified control across Day 0 to Day 2 of operation, with awareness across tenants and full visibility into both the front-end network handling user and application traffic, and the back-end network carrying GPU-to-GPU, storage, and distributed training traffic.

That end-to-end validation is what turns a shared AI factory into something enterprises, GPU cloud providers, and neo-cloud operators can actually run at scale, since every combination across the AI infrastructure stack gets checked and kept current through ongoing regression testing rather than assumed to work.

The overall thrust is toward making the difficult process of creating AI networks repeatable by the act of designing, validating and deploying at scale.

Explore the technical workflow:

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